DF-LoGiT: Data-Free Logic-Gated Backdoor Attacks in Vision Transformers

• URL: http://arxiv.org/abs/2602.03040v1
• Date: Tue, 03 Feb 2026 03:06:41 GMT
• Title: DF-LoGiT: Data-Free Logic-Gated Backdoor Attacks in Vision Transformers
• Authors: Xiaozuo Shen, Yifei Cai, Rui Ning, Chunsheng Xin, Hongyi Wu,
• Abstract summary: This paper introduces Data-Free Logic-Gated Backdoor Attacks (DF-LoGiT)<n> DF-LoGiT exploits ViT's native multi-head architecture to realize a logic-gated compositional trigger, enabling a stealthy and effective backdoor.<n>We show that DF-LoGiT achieves near-100% attack success with negligible degradation in benign accuracy and remains robust against representative classical and ViT-specific defenses.
• Score: 15.213392274245018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread adoption of Vision Transformers (ViTs) elevates supply-chain risk on third-party model hubs, where an adversary can implant backdoors into released checkpoints. Existing ViT backdoor attacks largely rely on poisoned-data training, while prior data-free attempts typically require synthetic-data fine-tuning or extra model components. This paper introduces Data-Free Logic-Gated Backdoor Attacks (DF-LoGiT), a truly data-free backdoor attack on ViTs via direct weight editing. DF-LoGiT exploits ViT's native multi-head architecture to realize a logic-gated compositional trigger, enabling a stealthy and effective backdoor. We validate its effectiveness through theoretical analysis and extensive experiments, showing that DF-LoGiT achieves near-100% attack success with negligible degradation in benign accuracy and remains robust against representative classical and ViT-specific defenses.

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